Based on research in 2024, 66% of B2B marketers globally are already using generative AI—a 20% year-over-year increase. If you’re reading this, you likely understand that this technology can help your marketing efforts. However, according to the research and ongoing discussions, many struggle to utilize the technology effectively, with 43% of marketers citing a lack of skills as a key barrier to entry.
How to use generative AI in B2B digital marketing
When it comes to generative AI platforms like OpenAI (ChatGPT), Perplexity, Adobe (Firefly), and Anthropic (Claude)—plus others focusing on niche applications—the real question is: what are they truly good for? Sure, you can request blog post ideas, get your morning affirmations when your boss doesn’t provide them, or generate some copywriting and images. But that seems like a light use case for such powerful technology.
Let’s explore three real-life examples of how we use these platforms to enhance marketing operations with higher quality, efficiency, and ROI.
1. Generate lead magnets 30x faster
Creating lead magnets, such as white papers, is an excellent way to generate leads and offer valuable material to interested prospects. However, traditional creation involves a lengthy and expensive process. While conventional white papers cost €10,000–15,000 to produce (and €20,000 with promotions), we’ve streamlined the production time from 50–100 hours to just 3 hours—from initial concept to running LinkedIn ads.
Our approach achieved a lead cost of €23 per lead (media cost), with over 90% qualifying as Marketing Qualified Leads—proving remarkably successful.
So, how can you do it? It’s actually quite easy.
- Train CustomGPT for white paper content generation on ChatGPT
- Create a white paper template in Google Slides
- Feed relevant blog posts and materials to the trained CustomGPT model
- Copy-paste the generated content into the template
- Use LLM platforms to generate initial ad variations and make them your own
Sure, your results may vary based on what you’re aiming for. But the idea here isn’t to say you will get it to 3 hours. You’ll definitely be faster than before and will find ways to scale.
2. Access custom dossiers and research into customer segments and markets
One of the unsung heroes in the latest development of many LLM platforms is their Deep Research functionality. This feature uses advanced reasoning and analysis to deliver comprehensive answers in minutes that would take analysts days to compile.
Use this functionality to better understand markets, get detailed dossiers on client companies, and identify the most suitable prospects for account-based marketing approaches.
This application requires more skill—you’ll need an analytical mind and experience in crafting effective prompts. But it’s worth the effort. For minimal hallucinations and maximum accuracy, Perplexity is one of the recommended platforms, though many others offer similar capabilities. Always verify the data by checking sources and asking follow-up questions.
3. Create an army of little AI helpers
Training your own CustomGPTs with your organization’s materials for specific tasks like data analysis and prospect sourcing is a powerful way to use these platforms. This is why specialized AI tools often outperform general-purpose ones: specific beats generic for targeted tasks.
Simply identify repetitive tasks, invest in a ChatGPT Plus subscription, and train your own GPTs. It’s a worthwhile investment on multiple levels. If you want to try one out, have a look at our Marketing Buddy.
Keep using generative AI as a tool, not a playtime activity disguised as work
Using these platforms can feel like a scattershot approach. Many users expend significant computational resources—as one prompt uses nearly as much energy as fully charging a phone—on mundane tasks that neither save time nor improve quality.
I encourage you to push beyond basic applications like generating blog ideas, writing ad copy, or building slides. Instead, focus on marketing operations that truly benefit from AI, such as analyzing large datasets efficiently and other tasks listed in this post.